A warming-induced rainfall heterogeneity accelerates landscape evolution
Bibliographic record
Abstract
The hydro-morphological response of a catchment is highly dependent on rainfall properties, including rainfall intensity, storm duration and frequency, and the timing of those events. Furthermore, rainfall spatial variability impacts streamflow, erosion, and sediment transport, and is explored primarily in the context of heavy rainfall triggering floods and rapid morphological changes on hillslopes and in channels. In order to examine the potential effects of warming on hydro-morphological responses, we first examined how changes in air temperature are affecting the spatial structure of rainfall. We observed that heterogeneity increases as temperatures rise. Then, we investigated the sensitivity of fast hydro-morphological responses to increasing temperatures and rainfall heterogeneity scenarios by simulating an extreme rainfall event that occurred in August 2005 in the Kleine Emme stream in Switzerland. The results show that rainfall heterogeneity has a greater impact on erosion processes than simply intensifying high rainfall intensities. We also looked at how changes in rainfall patterns affect landscape evolution over hundreds of years at the catchment scale. Multiple realizations of hourly rainfall fields, each with a different spatial distribution but identical in all other respects, were simulated using a stochastic weather generator, and the impact of the storm heterogeneity on catchment morphology was assessed using a landscape evolution model (CAESAR-Lisflood). We found that erosion and deposition rates increased and net erosion and deposition areas changed (increased and decreased, respectively) when the rain became less uniform in space. Increasing temperatures and rainfall heterogeneity resulted in longer, deeper, and more branched gullies. The results of these studies indicate that heterogeneity in rainfall spatial patterns accelerates landscape development even when rainfall volumes and temporal structures are identical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".